dataclasses — Data Classes — Python 3.8.20 documentation (original) (raw)

Source code: Lib/dataclasses.py

This module provides a decorator and functions for automatically adding generated special methods such as __init__() and__repr__() to user-defined classes. It was originally described in PEP 557.

The member variables to use in these generated methods are defined using PEP 526 type annotations. For example this code:

from dataclasses import dataclass

@dataclass class InventoryItem: """Class for keeping track of an item in inventory.""" name: str unit_price: float quantity_on_hand: int = 0

def total_cost(self) -> float:
    return self.unit_price * self.quantity_on_hand

Will add, among other things, a __init__() that looks like:

def init(self, name: str, unit_price: float, quantity_on_hand: int = 0): self.name = name self.unit_price = unit_price self.quantity_on_hand = quantity_on_hand

Note that this method is automatically added to the class: it is not directly specified in the InventoryItem definition shown above.

New in version 3.7.

Module-level decorators, classes, and functions

@ dataclasses. dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)

This function is a decorator that is used to add generatedspecial methods to classes, as described below.

The dataclass() decorator examines the class to findfields. A field is defined as class variable that has atype annotation. With two exceptions described below, nothing in dataclass()examines the type specified in the variable annotation.

The order of the fields in all of the generated methods is the order in which they appear in the class definition.

The dataclass() decorator will add various “dunder” methods to the class, described below. If any of the added methods already exist on the class, the behavior depends on the parameter, as documented below. The decorator returns the same class that is called on; no new class is created.

If dataclass() is used just as a simple decorator with no parameters, it acts as if it has the default values documented in this signature. That is, these three uses of dataclass() are equivalent:

@dataclass class C: ...

@dataclass() class C: ...

@dataclass(init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False) class C: ...

The parameters to dataclass() are:

fields may optionally specify a default value, using normal Python syntax:

@dataclass class C: a: int # 'a' has no default value b: int = 0 # assign a default value for 'b'

In this example, both a and b will be included in the added__init__() method, which will be defined as:

def init(self, a: int, b: int = 0):

TypeError will be raised if a field without a default value follows a field with a default value. This is true either when this occurs in a single class, or as a result of class inheritance.

dataclasses. field(*, default=MISSING, default_factory=MISSING, repr=True, hash=None, init=True, compare=True, metadata=None)

For common and simple use cases, no other functionality is required. There are, however, some dataclass features that require additional per-field information. To satisfy this need for additional information, you can replace the default field value with a call to the provided field() function. For example:

@dataclass class C: mylist: List[int] = field(default_factory=list)

c = C() c.mylist += [1, 2, 3]

As shown above, the MISSING value is a sentinel object used to detect if the default and default_factory parameters are provided. This sentinel is used because None is a valid value for default. No code should directly use the MISSINGvalue.

The parameters to field() are:

If the default value of a field is specified by a call tofield(), then the class attribute for this field will be replaced by the specified default value. If no default is provided, then the class attribute will be deleted. The intent is that after the dataclass() decorator runs, the class attributes will all contain the default values for the fields, just as if the default value itself were specified. For example, after:

@dataclass class C: x: int y: int = field(repr=False) z: int = field(repr=False, default=10) t: int = 20

The class attribute C.z will be 10, the class attributeC.t will be 20, and the class attributes C.x andC.y will not be set.

class dataclasses. Field

Field objects describe each defined field. These objects are created internally, and are returned by the fields()module-level method (see below). Users should never instantiate aField object directly. Its documented attributes are:

Other attributes may exist, but they are private and must not be inspected or relied on.

dataclasses. fields(class_or_instance)

Returns a tuple of Field objects that define the fields for this dataclass. Accepts either a dataclass, or an instance of a dataclass. Raises TypeError if not passed a dataclass or instance of one. Does not return pseudo-fields which are ClassVar or InitVar.

dataclasses. asdict(instance, *, dict_factory=dict)

Converts the dataclass instance to a dict (by using the factory function dict_factory). Each dataclass is converted to a dict of its fields, as name: value pairs. dataclasses, dicts, lists, and tuples are recursed into. For example:

@dataclass class Point: x: int y: int

@dataclass class C: mylist: List[Point]

p = Point(10, 20) assert asdict(p) == {'x': 10, 'y': 20}

c = C([Point(0, 0), Point(10, 4)]) assert asdict(c) == {'mylist': [{'x': 0, 'y': 0}, {'x': 10, 'y': 4}]}

Raises TypeError if instance is not a dataclass instance.

dataclasses. astuple(instance, *, tuple_factory=tuple)

Converts the dataclass instance to a tuple (by using the factory function tuple_factory). Each dataclass is converted to a tuple of its field values. dataclasses, dicts, lists, and tuples are recursed into.

Continuing from the previous example:

assert astuple(p) == (10, 20) assert astuple(c) == ([(0, 0), (10, 4)],)

Raises TypeError if instance is not a dataclass instance.

dataclasses. make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)

Creates a new dataclass with name cls_name, fields as defined in fields, base classes as given in bases, and initialized with a namespace as given in namespace. fields is an iterable whose elements are each either name, (name, type), or (name, type, Field). If just name is supplied,typing.Any is used for type. The values of init,repr, eq, order, unsafe_hash, and frozen have the same meaning as they do in dataclass().

This function is not strictly required, because any Python mechanism for creating a new class with __annotations__ can then apply the dataclass() function to convert that class to a dataclass. This function is provided as a convenience. For example:

C = make_dataclass('C', [('x', int), 'y', ('z', int, field(default=5))], namespace={'add_one': lambda self: self.x + 1})

Is equivalent to:

@dataclass class C: x: int y: 'typing.Any' z: int = 5

def add_one(self):
    return self.x + 1

dataclasses. replace(instance, **changes)

Creates a new object of the same type of instance, replacing fields with values from changes. If instance is not a Data Class, raises TypeError. If values in changes do not specify fields, raises TypeError.

The newly returned object is created by calling the __init__()method of the dataclass. This ensures that__post_init__(), if present, is also called.

Init-only variables without default values, if any exist, must be specified on the call to replace() so that they can be passed to__init__() and __post_init__().

It is an error for changes to contain any fields that are defined as having init=False. A ValueError will be raised in this case.

Be forewarned about how init=False fields work during a call toreplace(). They are not copied from the source object, but rather are initialized in __post_init__(), if they’re initialized at all. It is expected that init=False fields will be rarely and judiciously used. If they are used, it might be wise to have alternate class constructors, or perhaps a customreplace() (or similarly named) method which handles instance copying.

dataclasses. is_dataclass(class_or_instance)

Return True if its parameter is a dataclass or an instance of one, otherwise return False.

If you need to know if a class is an instance of a dataclass (and not a dataclass itself), then add a further check for not isinstance(obj, type):

def is_dataclass_instance(obj): return is_dataclass(obj) and not isinstance(obj, type)

Post-init processing

The generated __init__() code will call a method named__post_init__(), if __post_init__() is defined on the class. It will normally be called as self.__post_init__(). However, if any InitVar fields are defined, they will also be passed to __post_init__() in the order they were defined in the class. If no __init__() method is generated, then__post_init__() will not automatically be called.

Among other uses, this allows for initializing field values that depend on one or more other fields. For example:

@dataclass class C: a: float b: float c: float = field(init=False)

def __post_init__(self):
    self.c = self.a + self.b

See the section below on init-only variables for ways to pass parameters to __post_init__(). Also see the warning about howreplace() handles init=False fields.

Class variables

One of two places where dataclass() actually inspects the type of a field is to determine if a field is a class variable as defined in PEP 526. It does this by checking if the type of the field istyping.ClassVar. If a field is a ClassVar, it is excluded from consideration as a field and is ignored by the dataclass mechanisms. Such ClassVar pseudo-fields are not returned by the module-level fields() function.

Init-only variables

The other place where dataclass() inspects a type annotation is to determine if a field is an init-only variable. It does this by seeing if the type of a field is of type dataclasses.InitVar. If a field is an InitVar, it is considered a pseudo-field called an init-only field. As it is not a true field, it is not returned by the module-level fields() function. Init-only fields are added as parameters to the generated __init__() method, and are passed to the optional __post_init__() method. They are not otherwise used by dataclasses.

For example, suppose a field will be initialized from a database, if a value is not provided when creating the class:

@dataclass class C: i: int j: int = None database: InitVar[DatabaseType] = None

def __post_init__(self, database):
    if self.j is None and database is not None:
        self.j = database.lookup('j')

c = C(10, database=my_database)

In this case, fields() will return Field objects for i andj, but not for database.

Frozen instances

It is not possible to create truly immutable Python objects. However, by passing frozen=True to the dataclass() decorator you can emulate immutability. In that case, dataclasses will add__setattr__() and __delattr__() methods to the class. These methods will raise a FrozenInstanceError when invoked.

There is a tiny performance penalty when using frozen=True:__init__() cannot use simple assignment to initialize fields, and must use object.__setattr__().

Inheritance

When the dataclass is being created by the dataclass() decorator, it looks through all of the class’s base classes in reverse MRO (that is, starting at object) and, for each dataclass that it finds, adds the fields from that base class to an ordered mapping of fields. After all of the base class fields are added, it adds its own fields to the ordered mapping. All of the generated methods will use this combined, calculated ordered mapping of fields. Because the fields are in insertion order, derived classes override base classes. An example:

@dataclass class Base: x: Any = 15.0 y: int = 0

@dataclass class C(Base): z: int = 10 x: int = 15

The final list of fields is, in order, x, y, z. The final type of x is int, as specified in class C.

The generated __init__() method for C will look like:

def init(self, x: int = 15, y: int = 0, z: int = 10):

Default factory functions

If a field() specifies a default_factory, it is called with zero arguments when a default value for the field is needed. For example, to create a new instance of a list, use:

mylist: list = field(default_factory=list)

If a field is excluded from __init__() (using init=False) and the field also specifies default_factory, then the default factory function will always be called from the generated__init__() function. This happens because there is no other way to give the field an initial value.

Mutable default values

Python stores default member variable values in class attributes. Consider this example, not using dataclasses:

class C: x = [] def add(self, element): self.x.append(element)

o1 = C() o2 = C() o1.add(1) o2.add(2) assert o1.x == [1, 2] assert o1.x is o2.x

Note that the two instances of class C share the same class variable x, as expected.

Using dataclasses, if this code was valid:

@dataclass class D: x: List = [] def add(self, element): self.x += element

it would generate code similar to:

class D: x = [] def init(self, x=x): self.x = x def add(self, element): self.x += element

assert D().x is D().x

This has the same issue as the original example using class C. That is, two instances of class D that do not specify a value forx when creating a class instance will share the same copy ofx. Because dataclasses just use normal Python class creation they also share this behavior. There is no general way for Data Classes to detect this condition. Instead, dataclasses will raise aTypeError if it detects a default parameter of type list,dict, or set. This is a partial solution, but it does protect against many common errors.

Using default factory functions is a way to create new instances of mutable types as default values for fields:

@dataclass class D: x: list = field(default_factory=list)

assert D().x is not D().x

Exceptions

exception dataclasses. FrozenInstanceError

Raised when an implicitly defined __setattr__() or__delattr__() is called on a dataclass which was defined withfrozen=True. It is a subclass of AttributeError.